System and method for medical image based cardio-embolic stroke risk prediction

Abstract

The invention provides a system and method for medical image based cardio-embolic stroke risk prediction. A system and method for medical image based patient-specific ischemic stroke risk prediction is disclosed. Left atrium (LA) and left atrium appendage (LAA) measurements are extracted from medical image data of a patient. Derived metrics for the LA and LAA of the patient are computed using a patient-specific computational model of cardiac function based on the LA and LAA measurements extracted from the medical image data of the patient. A stroke risk score for the patient is calculated based on the extracted LA and LAA measurements and the computed derived metrics for the LA and LAA of the patient using a trained machine learning based classifier, which inputs the extracted LA and LAA measurements and the computed derived metrics for the LA and LAA as features.

Description

[0001] This application claims the benefit of US Provisional Application No. 62 / 301,861, filed March 1, 2016, the disclosure of which is incorporated herein by reference in its entirety. Background technique [0002] The present invention relates to patient-specific stroke risk prediction, and more particularly to patient-specific cardioembolic stroke risk prediction based on medical images. [0003] Stroke is the leading cause of adult disability and the fifth leading cause of death in the United States. There are two types of stroke, hemorrhagic and ischemic, with approximately 13% of strokes occurring being hemorrhagic and 87% ischemic. Ischemic strokes can in turn be of the embolic (~20%) or thrombotic (~80%) type. In an embolic stroke, a blood clot or platelet fragment forms somewhere in the body (usually the heart) and travels to the brain, where it blocks a small blood vessel. In a thrombotic stroke, a blood clot forms inside an artery that supplies blood to the brain...

AI Technical Summary

Problems solved by technology

However, risk indicators extracted from such assessments are limited to simple statistical indicators with large variance, which are usually based on clinical longitudinal data

This risk prediction method has several disadvantages, including temporal plasticity (i.e., uncertainty) and a large range of these indicators, and weak / reduced patient specificity

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